RIASSUNTO
Situation awareness in maritime environment entails early detection and classification of maritime targets of varying sizes, depths, shapes, textures, and contrasts. Thus, this paper describes a novel deep learning based maritime situation awareness approach using high-definition video. Maritime object detection is achieved in three main steps. At first, a key region based tracking algorithm allows to, dynamically and parsimoniously, extract high-quality region proposals mainly focalized around rigid (i.e.; potential object) video locations. The latter are, further, fed into a Fast-RCNN for carrying out objectness detection and box regression. Finally, a mere box post-regression operation enables to extract maritime objects. Furthermore, the found object detections are fed into a second classification RCNN, specifically, trained to recognize up to 40 vessel classes. Our experiments have shown that the proposed approach achieves state of the art speed and accuracy.